Characterising the spatio-temporal dynamics of pathogens in natura is key to ensuring their efficient prevention and control. However, it is notoriously difficult to estimate dispersal parameters at scales that are relevant to real epidemics. Epidemiological surveys can provide informative data, but parameter estimation can be hampered when the timing of the epidemiological events is uncertain, and in the presence of interactions between disease spread, surveillance, and control. Further complications arise from imperfect detection of disease, and from the computationally intractable number of data on individual hosts arising from landscape-level surveys. Here, we present a Bayesian framework that overcomes these barriers by integrating over associated uncertainties in a model explicitly combining the processes of disease dispersal, surveillance and control. Using a novel computationally efficient approach to account for patch geometry, we demonstrate that disease dispersal distances can be estimated accurately in a fragmented landscape when disease control is ongoing. Applying this model to data for an aphid-borne virus ( Plum pox virus ) surveyed for 15 years over 600 orchards, we obtain the first estimate of the distribution of the flight distances of infectious aphids at the landscape scale. Most infectious aphids leaving a tree land beyond the bounds of a 1-ha orchard (50% of flights terminate within about 90 m). Moreover, long-distance flights are not rare (10% of flights exceed 1 km). By their impact on our quantitative understanding of winged aphids dispersal, these results can inform the design of management strategies for plant viruses, which are mainly aphid-borne.